RACAS：通过单一代理系统控制多样化机器人
RACAS: Controlling Diverse Robots With a Single Agentic System
AI总结：
RACAS通过单一代理系统实现跨平台机器人控制，无需修改代码或模型，有效降低机器人原型开发难度。
摘要：
许多机器人平台通过API允许外部软件控制其执行器并读取其传感器。然而，从这些低级接口过渡到高级自主行为需要复杂的管道，其组件需要不同的专业知识领域。现有的方法要么需要为每个新的机体重新训练，要么只在结构上相似的平台上得到验证。我们引入了RACAS（通过代理系统实现机器人无关控制），这是一种协作的代理架构，其中三个基于LLM/VLM的模块（监控器、控制器和记忆 curator）通过自然语言相互沟通，以提供闭环机器人控制。RACAS只需要一个机器人自然语言描述、可用动作的定义和任务规范；无需修改源代码、模型权重或奖励函数即可在不同平台上切换。我们使用轮式地面机器人、最近发表的新型多关节机械臂和水下车辆对RACAS进行了评估。RACAS在这些截然不同的平台上一致解决了所有分配的任务，展示了代理AI在显著降低机器人解决方案原型化障碍方面的潜力。
英文摘要：
Many robotic platforms expose an API through which external software can command their actuators and read their sensors. However, transitioning from these low-level interfaces to high-level autonomous behaviour requires a complicated pipeline, whose components demand distinct areas of expertise. Existing approaches to bridging this gap either require retraining for every new embodiment or have only been validated across structurally similar platforms. We introduce RACAS (Robot-Agnostic Control via Agentic Systems), a cooperative agentic architecture in which three LLM/VLM-based modules (Monitors, a Controller, and a Memory Curator) communicate exclusively through natural language to provide closed-loop robot control. RACAS requires only a natural language description of the robot, a definition of available actions, and a task specification; no source code, model weights, or reward functions need to be modified to move between platforms. We evaluate RACAS on several tasks using a wheeled ground robot, a recently published novel multi-jointed robotic limb, and an underwater vehicle. RACAS consistently solved all assigned tasks across these radically different platforms, demonstrating the potential of agentic AI to substantially reduce the barrier to prototyping robotic solutions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
